Study on the efficiency of nursing service and the allocation of human resources in blood collection center based on visiting volume
Bibliographic record
Abstract
Objective To provide scientific basis for evaluating the efficiency of nursing service and reasonable allocation of human resources in blood collection by analyzing the time variation of visiting time in blood collection center. Methods Through the retrospective analysis of 2016 in different seasons and different blood centers working day and different time visits, obtains the variation of visits time; using a queuing analysis to evaluate different visits under the condition of nursing service efficiency and puts forward the scheme of optimal allocation of human resources. Results The blood center visits within one year of the first quarter of the lowest, the second quarter increased significantly, reaching the peak at the third quarter, the fourth quarter is relatively reduced; the work on Monday, Thursday, two or three high, five less; morning visits in different time was significantly higher than that in the afternoon, the morning peak on the 8:00-9:00 and 14:00-15:00 concentration in the afternoon peak; the situation of human resource allocation at this stage of blood centers, visits the peak season of nurse service intensity and low efficiency, the patients waiting for a long time, visits the low peak season, the nurse service intensity was low, idle for a long time, resulting in a waste of resources, quantitative configuration after the nurse service reasonable intensity, high work efficiency, effective treatment time of patients increased. Conclusions According to the law of time variation, the nursing service efficiency can be scientifically analyzed and the nursing manpower should be quantified to provide a scientific basis for hospital management. Key words: Visiting quantity; Nursing care; Personnel administration, hospital
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".